Method and apparatus for collaborative filtering of card member transactions

ABSTRACT

The disclosed system allows a credit or charge card issuer to provide its card members with a list of merchants, products, services, vacation destinations or other offerings that might be of interest based on the purchases of similar card members. In one instance, this process looks at all card members that made purchases at a merchant and then it identifies all other merchants in the same category where those card members also made purchases. The associated merchants are ranked based on largest number of shared card members and the top results may be shared with card members or merchants in order to enhance promotions, card use and marketing.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority to, U.S. Ser.No. 12/907,748 filed on Oct. 19, 2010 entitled “Method and Apparatus forCollaborative Filtering of Card Member Transactions.” The '748application is a continuation of and claims priority to, U.S. Pat. No.7,848,950 issued Dec. 7, 2010 (aka U.S. Ser. No. 11/315,262 filed onDec. 23, 2005) entitled “Method and Apparatus for CollaborativeFiltering of Card Member Transactions.” The '950 patent claims priorityunder 35 U.S.C. §119(e) to U.S. Provisional Patent Application No.60/639,472, filed Dec. 28, 2004. All of which are incorporated byreference herein in their entirety.

BACKGROUND

1. Field of the Invention

This invention generally relates to financial data processing, and inparticular it relates to incentive and promotional programs.

2. Related Art

Consumers are constantly searching for information on products andservices that may be of interest to them, but with which they have noactual experience. They typically seek independent information beforemaking certain purchases. Various sources provide reports on productsand services to satisfy this consumer demand for information. Forexample, ZAGATS provides ratings on restaurants and CONSUMER REPORTSprovides detailed listings on product quality and customer satisfaction.When making a purchase of a selected product on web sites such asAMAZON.COM, information is typically provided about other productspurchased by customers who have also purchased the selected product.

Consumers frequently use credit, debit, stored value or charge cards(collectively referred to herein as credit instruments) in transactionswith various merchants. This data is collected and processed en mass bycredit providers for billing purposes and the like. However, little hasbeen done to harness such card member transaction details for marketingpurposes.

BRIEF DESCRIPTION

Accordingly, the present disclosure introduces a system for processingfinancial transaction data, referred to herein as collaborativefiltering, in which transaction data between card members and merchantsis captured and analyzed for marketing purposes.

According to various embodiments of the disclosed processes, a pluralityof merchants having transactions with card members are grouped based onan industry code and their geographic location. For each merchant, thesystem determines its total number of financial transactions involvingcard members over a period of time. The system then selects a firstmerchant from the group and identifies all card members that have had atleast one financial transaction with the first merchant over the periodof time. The system next determines, for each remaining merchant in thegroup, the number of card members having at least one financialtransaction with both the first merchant and the remaining merchant overthe period of time. The system then ranks each remaining merchant basedon a ratio of: (i) the number of card members having at least onefinancial transaction with both the first merchant and the remainingmerchant over the period of time to (ii) the number of financialtransactions involving card members over the period of time. Acorrective factor may be introduced to eliminate merchants havingrelatively few card member transactions. The system then reports one ormore of the highest-ranked remaining merchants in which card membershave had at least one financial transaction with both merchants over theperiod of time. The ranking of merchants may then be provided to cardmembers or interested merchants for marketing purposes.

In various examples, the processes disclosed herein are particularlyuseful for identifying restaurants or vacation destinations that may beof interest to card members, but may be applied to any of a variety ofproducts, services and offerings.

Further features and advantages of the present invention as well as thestructure and operation of various embodiments of the present inventionare described in detail below with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention will become more apparent from thedetailed description set forth below when taken in conjunction with thedrawings. The left-most digit of a reference number identifies thedrawing in which the reference number first appears.

FIG. 1 is a block diagram of an exemplary computer network over whichthe processes of the present disclosure may be performed.

FIG. 2 is a flowchart of an exemplary collaborative filtering processperformed over the network of FIG. 1.

FIG. 3 is a diagram of an exemplary collaborative filtering database foruse with the process of FIG. 2,

FIG. 4 is a diagram of exemplary ranking results using the collaborativefiltering process of FIG. 2.

DETAILED DESCRIPTION Overview

The collaborative filtering processes now introduced allows a credit,debit, stored value or charge card provider, such as AMERICAN EXPRESS,to analyze financial transaction data involving its card members (i.e.,holders of a particular brand of credit instrument) that is typicallyused only for billing purposes. The system also harnesses suchinformation in order to assist consumers in making other purchases atmerchants that have appealed to other (similarly-situated) card members.According to such processes, merchants who accept a particular brand ofcredit instrument are grouped by location and industry code. Financialtransactions between card members and the various merchants within oneor more groups are analyzed to identify those merchants with common cardmember patronage, and to further rank merchants within a group that havehad common patronage among card members. The ranking of similarmerchants may be reported to card members who have made at least onepurchase from a merchant within the group in order to assist the cardmembers in making their purchases at similar merchants. Such reportingmay be accompanied by discounts on purchases at such other merchants, ifdesired. The ranking of merchants may also be communicated to themerchants themselves (in a manner such that card member privacy withoutis not violated), who may then properly use such information for theirmarketing purposes.

Exemplary Systems and Processes

Referring now to FIGS. 1-4, wherein similar components of the presentdisclosure are referenced in like manner, and wherein variousembodiments of a method and system for collaborative filtering of cardmember transactions are disclosed.

Turning to FIG. 1, there is depicted an exemplary computer network 100over which the transmission of financial transaction data as describedherein may be accomplished, using any of a variety of availablecomputing components for processing such data. Such components mayinclude a credit provider server 102, which may be a computer, such asan enterprise server of the type commonly manufactured by SUNMICROSYSTEMS. The credit provider server 102 has appropriate internalhardware, software, processing, memory and network communicationcomponents, which enables it to perform the functions described herein.General software applications may include the SOLARIS operating systemand SYBASE IQ data management and analysis tools. The credit providerserver 102 stores financial transaction data in appropriate memory andprocesses the same according to the processes described herein usingprogramming instructions that may be provided in any of a variety ofuseful machine programming languages. It should be readily apparent thatany number of other computing systems and software may be used toaccomplish the processes described herein.

The credit provider server 102 may, in turn, be in operativecommunication with any number of other external servers 104, which maybe computers or servers of similar or compatible functionalconfiguration. These external servers 104 may gather and providefinancial transactions data, as described herein, and transmit the samefor processing and analysis by the credit provider server 102. Such datatransmissions may occur for example over the Internet or by any otherknown communications infrastructure, such as a local area network, awide area network, a wireless network, a fiber-optic network, or anycombination or interconnection of the same. Such communications may alsobe transmitted in an encrypted or otherwise secure format, in any of awide variety of known manners. Each of the external servers 104 may beoperated by either common or independent entities, and in certainembodiment may represent point-of-sale terminals where card membertransactions are initiated, or may be servers operated by credit cardclearinghouses that typically process credit transactions.

Turning now to FIG. 2, therein is depicted an exemplary collaborativefiltering process 200 performed by the credit provider server 102 usingthe financial transaction data obtained by and transmitted from theexternal servers 104.

The process 200 commences with the capture of financial transaction datainvolving a plurality of card members and merchants over a period oftime (step 202). Such financial transaction data may include, but is notlimited to, an identification of the card member (such as by name and/oraccount number), an identification of the merchant (such as by name ormerchant identification number), a financial amount of the transaction,and the date of the transaction. The period of time may be for example amonth, a quarter, a year or any other desired period of time. The creditprovider server 102 may store such received data in a suitable databaseformat for analysis as described herein.

Next, the credit provider server 102 groups similar merchants havingtransactions with card members according to their geographic locationand an applicable industry code (step 204). The grouping of themerchants by geographic location may be accomplished according to thezip code, street address, city, metropolitan area (MSA) or county inwhich the merchants reside, which is typically readily available tocredit providers. The grouping by geographic location ensures that cardmembers who have frequented a merchant in that location may be amenableto visiting other merchants in the same location.

The further grouping of merchants by industry code ensures that themerchants within the group offer similar products and services. Theindustry code may be a Standard Industry Classification (SIC) code thatmay be assigned to merchants by a government agency. The industry codemay further be a proprietary classification code assigned by a creditprovider, issuer, or acquiror to a class of merchants to uniquelyidentify the products or services offered by such merchants.

Next, at step 206, a group of merchants is selected for analysis whereinthe number of financial transactions with card members is determined andstored for each merchant in the group. This information may then bestored by the credit provider server 102 in a database, such as database300 described below with respect to FIG. 3.

Next, at step 208, a merchant is selected from the group, and each cardmember having at least one transaction with that merchant is identifiedfrom the stored financial transaction data.

Next, at step 210, it is determined whether any other merchants withinthat group have had transaction with any of the card members identifiedin step 208 above. Those merchants having transactions with common cardmembers are identified and ranked based on a ratio of the number ofnumber of transactions with common card members to the total number oftransactions with all card members determined in step 206 above.

One exemplary method for ranking merchants will now be described withreference to FIG. 3 wherein an exemplary merchant ranking database 300used by the collaborative filtering process 200 is depicted. Thedatabase 300 has a number of fields represented by columns in FIG. 3 anda number of database records, represented as rows within FIG. 3. Thisdatabase 300 may include: (i) a first merchant identifier field 302 forstoring an identification of a first merchant being analyzed by thecollaborative filtering process 200; (ii) a second merchant identifierfield 200 for storing an identification of similar merchants havingtransactions with common cardholders; (iii) a number of common cardmembers field 306 for storing a number of card members who havefrequented both the merchants identified in fields 302 and 304; a numberof total transactions field 308 for storing the total number of cardmember transactions involving the second merchant identified in field304; a ratio field 310 for storing the ratio of the value stored infield 306 to the value stored in field 308; and a ranking field thatstores the result of a ranking applied to the data in fields 306-308.

A problem exists with simply using the ratio value 310 to directly ranksimilar merchants within a group. This problem becomes apparent when thenumber of shared card members is low, or is close to the total number oftransactions at the second merchant. Either scenario, or a combinationof the two, would result in a ratio of nearly 1:1. However, particularlyin the case where there are few shared card member transactions, simplyusing the highest ratios may not be representative of a true correlationbetween the patronage of the first and second merchants.

Accordingly, a mathematical solution may be applied that discounts suchproblematic data. One such solution may be expressed as follows:

C=A+(10*B*(A−3))

-   -   where:    -   C is the value stored in field 312;    -   A is the value stored in field 306;    -   B is the ratio value stored in field 310, obtained by dividing        the value stored in field 306 by the value stored in field 308;        and    -   the value of (A-3) is set to zero if it results in zero or a        negative number.

It should be noted that, in one embodiment, the corrective factor10*B*(A−3) has been incorporated to discount coincidences from merchantshaving relatively few numbers of common card member transactions, andadd weight to those with higher ratios and numbers of commontransactions. The variables in the corrective factor were determined tobe suitable based on experimental data and may be altered or adjustedbased on empirical data resulting from actual use of the collaborativefiltering processes. Other suitable corrective factors may also beapplied.

Returning to the process 200, upon completion of the analysis in step210 above, each of the remaining merchants within the group are rankedaccording to the value stored in field 312 for them, wherein thehighest-ranked second merchant has the highest ranking value and thelowest-ranked second merchant has the lowest ranking value. The creditprovider server may rank only a threshold number of second merchants,such as the top five merchants.

These merchants may then be stored in a merchant ranking database 400shown in FIG. 4. An exemplary merchant ranking database 400 includes thefollowing fields: (i) a primary merchant field 402 for storing anidentification of a first merchant in a group; (ii) a highest rankedmerchant field 404 for storing the highest ranked merchant in the groupbased on its ranking; (iii) a second-highest ranked merchant field 404for storing an identification of the second highest ranked merchant inthe group; and (iv) third though fifth highest-ranked merchant fields408-412 for storing the respective appropriate merchant identifications.

The merchant rankings stored in database 400 may be reported to cardmembers in any of a variety of manners. In one example, thecollaborative filtering processes could be used to identify restaurantsthat a card member may wish to try. Suppose Card member A recently ateat a Sushi Restaurant in Manhattan. The collaborative filtering process200 could be used to identify and report the highest-ranked restaurants(based on similar industry codes, and therefore, similar services) whereother card members who have dined at the Sushi Restaurant have alsodined. Based on Card member A's patronage of the Sushi Restaurant, areport of these highest ranked restaurants may be provided with Cardmember A's billing statement or otherwise communicated to the cardmember by, for example, a separate mailing, electronic means (e.g.,e-mail) or telemarketing means.

in another example of the collaborative filtering process, card memberswho are identified as having vacationed in a certain destination couldbe informed of other top vacation destinations by other card members whohave also vacationed at that destination. Such vacation destinations mayor may not be grouped by similar geographic location or similarmerchants, but instead may simply be based on overall card memberpreferences.

In one embodiment, the ranking information produced by the collaborativefiltering process described herein may be provided to merchantsthemselves. For example, a restaurateur may learn that many customers ofa competing restaurant also tend to frequent their establishment. Therestaurateur may then offer to accept coupons from that competitor inorder to attract new customers.

The disclosed collaborative filtering processes solve several problemsby allowing a credit provider and merchants to customize promotions ormarketing offers, while at the same time providing a value-added benefitfor card members, by providing them with meaningful information aboutmerchants they may want to patronize due to patronage from other(presumably similarly-situated) card members who carry and utilize thesame particular brand of credit instrument. The process leverages theability to personalize information based on purchases already made bycard members. By providing such personalized information to cardmembers, a credit provider can expect to experience an increase inrevenues due to transactions that are encouraged by the collaborativefiltering process.

CONCLUSION

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to persons skilled inthe relevant art(s) that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentinvention (e.g., packaging and activation of other transaction cardsand/or use of batch activation processes). Thus, the present inventionshould not be limited by any of the above described exemplaryembodiments, but should be defined only in accordance with the followingclaims and their equivalents.

In addition, it should be understood that the figures and screen shotsillustrated in the attachments, which highlight the functionality andadvantages of the present invention, are presented for example purposesonly. The architecture of the present invention is sufficiently flexibleand configurable, such that it may be utilized (and navigated) in waysother than that shown in the accompanying figures.

Further, the purpose of the following Abstract is to enable the U.S.Patent and Trademark Office and the public generally, and especially thescientists, engineers and practitioners in the art who are not familiarwith patent or legal terms or phraseology, to determine quickly from acursory inspection the nature and essence of the technical disclosure ofthe application. The Abstract is not intended to be limiting as to thescope of the present invention in any way,

1. A method comprising: determining, by a computer-based ranking system,a first value for each pair of, a first merchant of a plurality ofmerchants, and each remaining merchant, wherein the first valuecorresponds to a number of account affiliates having at least onefinancial transaction with the first merchant and the remainingmerchants during a period of time; determining, by the computer-basedsystem, a second value for each of the remaining merchants of theplurality of merchants other than the first merchant, the second valuecorresponding to the number of account affiliates having at least onefinancial transaction with each of the remaining merchants during theperiod of time; determining, by the computer-based system, a third valuecorresponding to a ratio of the first value to the second value;determining, by the computer-based system, a fourth value based on thefirst value, the third value and a weighting factor; and ranking, by thecomputer-based system, each remaining merchant based on the fourthvalue.
 2. The method of claim 1, wherein the fourth value corresponds tothe third value multiplied by a corrective factor to account for asignificance of financial transactions.
 3. The method of claim 2,wherein the corrective factor comprises reducing the third value basedon a reduced significance of transactions occurring between the firstmerchant and merchants having a number of financial transactions below athreshold number with both the first merchant and each of the remainingmerchants during the period of time.
 4. The method of claim 3, whereinthe corrective factor comprises increasing the third value based on anincreased significance of transactions occurring between the firstmerchant and merchants having a number of financial transactions abovethe threshold number with both the first merchant and each of theremaining merchants during the period of time.
 5. The method of claim 2,wherein the corrective factor further comprises a calculated correctivefactor based on empirical data.
 6. The method of claim 1, wherein theranking further comprises ranking based on ratings of vacationdestinations received from account affiliates.
 7. The method of claim 1further comprising offering a discount on a purchase from at least oneof the remaining merchants to account affiliates having at least onefinancial transaction with the first merchant during the period of time.8. The method of claim 1, further comprising receiving informationcorresponding to a plurality of merchants.
 9. The method of claim 1,further comprising determining a group of the plurality of merchantsbased on an industry code and a geographic location.
 10. The method ofclaim 6, wherein vacation destinations visited by the account affiliatesare grouped using vacation destination characteristics.
 11. The methodof claim 1, further comprising receiving information corresponding to anumber of financial transactions involving account affiliates during theperiod of time.
 12. The method of claim 1, further comprisingdetermining, for each merchant of the plurality of merchants, thefinancial transactions involving the account affiliates during theperiod of time.
 13. The method claim 1, wherein the plurality ofmerchants are restaurateurs.
 14. The method claim 1, wherein theplurality of merchants are restaurateurs offering similar cuisine. 15.The method of claim 1, wherein the fourth value comprisesC=A+(WI*B*(A−W2)); wherein, C=the fourth value A=the first value B=thethird value W1=a first weighting factor W2=a second weighting factor.16. An article of manufacture including a non-transitory, tangiblecomputer readable storage medium having instructions stored thereonthat, in response to execution by a computer-based ranking system, causethe computer-based system to perform operations comprising: determining,by the computer-based system, a first value for each pair of a firstmerchant of a plurality of merchants, and each remaining merchant,wherein the first value corresponds to a number of account affiliateshaving at least one financial transaction with the first merchant andthe remaining merchants during a period of time; determining, by thecomputer-based system, a second value for each of the remainingmerchants of the plurality of merchants other than the first merchant,the second value corresponding to the number of account affiliateshaving at least one financial transaction with each of the remainingmerchants during the period of time; determining, by the computer-basedsystem, a third value corresponding to a ratio of the first value to thesecond value; determining, by the computer-based system, a fourth valuebased on the first value, the third value and a weighting factor; andranking, by the computer-based system, each remaining merchant based onthe fourth value.
 17. A system comprising: a processor for ranking; atangible, non-transitory memory communicating with the processor, thetangible, non-transitory memory having instructions stored thereon that,in response to execution by the processor, cause the processor toperform operations comprising: determining, by the processor, a firstvalue for each pair of a first merchant of a plurality of merchants, andeach remaining merchant, wherein the first value corresponds to a numberof account affiliates having at least one financial transaction with thefirst merchant and the remaining merchants during a period of time;determining, by the processor, a second value for each attic remainingmerchants of the plurality of merchants other than the first merchant,the second value corresponding to the number of account affiliateshaving at least one financial transaction with each of the remainingmerchants during the period of time; determining, by the processor, athird value corresponding to a ratio of the first value to the secondvalue; determining, by the processor, a fourth value based on the firstvalue, the third value and a weighting factor; and ranking, by theprocessor, each remaining merchant based on the fourth value.